Quest 9

What is MLOps and how does it differ from DevOps?

Describe the key stages of the MLOps lifecycle.

What is model drift, and how would you monitor for it in production?  

How would you implement a CI/CD pipeline for a machine learning model?  

What is the purpose of a feature store in an ML system?  

Explain the difference between Canary and Blue-Green deployment strategies for ML models.  

How does data version control impact the machine learning lifecycle?  

What are the challenges of maintaining consistent environments across the ML lifecycle, and how can containerization (e.g., Docker) address them?  

Explain how you have used Docker containers or Kubernetes in deploying machine learning models.  

What is the purpose of an orchestration tool like Airflow or Kubeflow in an ML pipeline?